cudaBayesreg: Parallel implementation of a Bayesian multilevel model for fMRI data analysis

Graphic processing units (gpus) are rapidly gaining maturity as powerful general parallel computing devices. a key feature in the development of modern gpus has been the advancement of the programming model and programming tools. compute unied device architecture (cuda) is a software platform for massively parallel high-performance computing on nvidia many-core gpus. in functional magnetic resonance imaging (fmri),the volume of the data to be processed,and the type of statistical analysis to perform call for high-performance computing strategies. in this work,we present the main features of the r-cuda package cudabayesreg which implements in cuda the core of a bayesian multilevel model for the analysis of brain fmri data. the statistical model implements a gibbs sampler for multilevel/hierarchical linear models with a normal prior. the main contribution for the increased performance comes from the use of separate threads for tting the linear regression model at each voxel in parallel. the r-cuda implementation of the bayesian model proposed here has been able to reduce signicantly the run-time processing of markov chain monte carlo (mcmc) simulations used in bayesian fmri data analyses. presently,cudabayesreg is only congured for linux systems with nvidia cuda support.